Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Network

Wi-Fi sensing uses radio-frequency signals from Wi-Fi devices to analyze environments, enabling tasks such as tracking people, detecting intrusions, and recognizing gestures. The rise of this technology is driven by the IEEE 802.11bf standard and growing demand for tools that can ensure privacy and operate through obstacles. However, the performance of Wi-Fi sensing is heavily influenced by environmental conditions, especially when extracting spatial and temporal features from the surrounding scene. A key challenge is achieving robust generalization across domains, ensuring stable performance even when the sensing environment changes significantly. This paper introduces a novel deep learning model for cross-domain adaptation of Wi-Fi signals, inspired by physical signal shielding. The model uses a Relativistic average Generative Adversarial Network (RaGAN) with Bidirectional Long Short-Term Memory (Bi-LSTM) architectures for both the generator and discriminator. To simulate physical shielding, an acrylic box lined with electromagnetic shielding fabric was constructed, mimicking a Faraday cage. Wi-Fi signal spectra were collected from various materials both inside (domain-free) and outside (domain-dependent) the box to train the model. A multi-class Support Vector Machine (SVM) was trained on domain-free spectra and tested on signals denoised by the RaGAN. The system achieved 96% accuracy and demonstrated strong material discrimination capabilities, offering potential for use in security applications to identify concealed objects based on their composition.

Tracking-Aided Multi-User MIMO Communications with Hybrid Reconfigurable Intelligent Surfaces

Hybrid Reconfigurable Intelligent Surfaces (HRISs) constitute a new paradigm that redefines smart metasurfaces, not only offering tunable reflections of incoming signals, but also incorporating signal reception and processing capabilities. In this paper, leveraging the simultaneous dual-functionality of HRISs, we propose a novel framework for tracking-aided multi-user Multiple-Input Multiple-Output (MIMO) communications. In particular, a joint design of the transmit multi-user precoding matrix together with the HRIS reflection and analog combining configurations is presented, with the objective to maximize the accuracy of position estimation of multiple mobile users while meeting their individual quality-of-service constraints for sensing-aided communications. The Cramer-Rao bound for the users' positioning parameters is derived together with a prediction approach based on the extended Kalman filter. Our simulation results showcase the efficacy of the proposed Integrated Sensing And Communications (ISAC) framework over various system configuration parameters.

Adversarial Attack for RGB-Event based Visual Object Tracking

Visual object tracking is a crucial research topic in the fields of computer vision and multi-modal fusion. Among various approaches, robust visual tracking that combines RGB frames with Event streams has attracted increasing attention from researchers. While striving for high accuracy and efficiency in tracking, it is also important to explore how to effectively conduct adversarial attacks and defenses on RGB-Event stream tracking algorithms, yet research in this area remains relatively scarce. To bridge this gap, in this paper, we propose a cross-modal adversarial attack algorithm for RGB-Event visual tracking. Because of the diverse representations of Event streams, and given that Event voxels and frames are more commonly used, this paper will focus on these two representations for an in-depth study. Specifically, for the RGB-Event voxel, we first optimize the perturbation by adversarial loss to generate RGB frame adversarial examples. For discrete Event voxel representations, we propose a two-step attack strategy, more in detail, we first inject Event voxels into the target region as initialized adversarial examples, then, conduct a gradient-guided optimization by perturbing the spatial location of the Event voxels. For the RGB-Event frame based tracking, we optimize the cross-modal universal perturbation by integrating the gradient information from multimodal data. We evaluate the proposed approach against attacks on three widely used RGB-Event Tracking datasets, i.e., COESOT, FE108, and VisEvent. Extensive experiments show that our method significantly reduces the performance of the tracker across numerous datasets in both unimodal and multimodal scenarios. The source code will be released on https://github.com/Event-AHU/Adversarial_Attack_Defense

LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring

Railway systems, particularly in Germany, require high levels of automation to address legacy infrastructure challenges and increase train traffic safely. A key component of automation is robust long-range perception, essential for early hazard detection, such as obstacles at level crossings or pedestrians on tracks. Unlike automotive systems with braking distances of ~70 meters, trains require perception ranges exceeding 1 km. This paper presents an deep-learning-based approach for long-range 3D object detection tailored for autonomous trains. The method relies solely on monocular images, inspired by the Faraway-Frustum approach, and incorporates LiDAR data during training to improve depth estimation. The proposed pipeline consists of four key modules: (1) a modified YOLOv9 for 2.5D object detection, (2) a depth estimation network, and (3-4) dedicated short- and long-range 3D detection heads. Evaluations on the OSDaR23 dataset demonstrate the effectiveness of the approach in detecting objects up to 250 meters. Results highlight its potential for railway automation and outline areas for future improvement.

Highly Accurate and Diverse Traffic Data: The DeepScenario Open 3D Dataset

Accurate 3D trajectory data is crucial for advancing autonomous driving. Yet, traditional datasets are usually captured by fixed sensors mounted on a car and are susceptible to occlusion. Additionally, such an approach can precisely reconstruct the dynamic environment in the close vicinity of the measurement vehicle only, while neglecting objects that are further away. In this paper, we introduce the DeepScenario Open 3D Dataset (DSC3D), a high-quality, occlusion-free dataset of 6 degrees of freedom bounding box trajectories acquired through a novel monocular camera drone tracking pipeline. Our dataset includes more than 175,000 trajectories of 14 types of traffic participants and significantly exceeds existing datasets in terms of diversity and scale, containing many unprecedented scenarios such as complex vehicle-pedestrian interaction on highly populated urban streets and comprehensive parking maneuvers from entry to exit. DSC3D dataset was captured in five various locations in Europe and the United States and include: a parking lot, a crowded inner-city, a steep urban intersection, a federal highway, and a suburban intersection. Our 3D trajectory dataset aims to enhance autonomous driving systems by providing detailed environmental 3D representations, which could lead to improved obstacle interactions and safety. We demonstrate its utility across multiple applications including motion prediction, motion planning, scenario mining, and generative reactive traffic agents. Our interactive online visualization platform and the complete dataset are publicly available at app.deepscenario.com, facilitating research in motion prediction, behavior modeling, and safety validation.

Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization

The rapid development of video surveillance systems for object detection, tracking, activity recognition, and anomaly detection has revolutionized our day-to-day lives while setting alarms for privacy concerns. It isn't easy to strike a balance between visual privacy and action recognition performance in most computer vision models. Is it possible to safeguard privacy without sacrificing performance? It poses a formidable challenge, as even minor privacy enhancements can lead to substantial performance degradation. To address this challenge, we propose a privacy-preserving image anonymization technique that optimizes the anonymizer using penalties from the utility branch, ensuring improved action recognition performance while minimally affecting privacy leakage. This approach addresses the trade-off between minimizing privacy leakage and maintaining high action performance. The proposed approach is primarily designed to align with the regulatory standards of the EU AI Act and GDPR, ensuring the protection of personally identifiable information while maintaining action performance. To the best of our knowledge, we are the first to introduce a feature-based penalty scheme that exclusively controls the action features, allowing freedom to anonymize private attributes. Extensive experiments were conducted to validate the effectiveness of the proposed method. The results demonstrate that applying a penalty to anonymizer from utility branch enhances action performance while maintaining nearly consistent privacy leakage across different penalty settings.

Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving

The significant achievements of pre-trained models leveraging large volumes of data in the field of NLP and 2D vision inspire us to explore the potential of extensive data pre-training for 3D perception in autonomous driving. Toward this goal, this paper proposes to utilize massive unlabeled data from heterogeneous datasets to pre-train 3D perception models. We introduce a self-supervised pre-training framework that learns effective 3D representations from scratch on unlabeled data, combined with a prompt adapter based domain adaptation strategy to reduce dataset bias. The approach significantly improves model performance on downstream tasks such as 3D object detection, BEV segmentation, 3D object tracking, and occupancy prediction, and shows steady performance increase as the training data volume scales up, demonstrating the potential of continually benefit 3D perception models for autonomous driving. We will release the source code to inspire further investigations in the community.

Robust Visual Servoing under Human Supervision for Assembly Tasks

We propose a framework enabling mobile manipulators to reliably complete pick-and-place tasks for assembling structures from construction blocks. The picking uses an eye-in-hand visual servoing controller for object tracking with Control Barrier Functions (CBFs) to ensure fiducial markers in the blocks remain visible. An additional robot with an eye-to-hand setup ensures precise placement, critical for structural stability. We integrate human-in-the-loop capabilities for flexibility and fault correction and analyze robustness to camera pose errors, proposing adapted barrier functions to handle them. Lastly, experiments validate the framework on 6-DoF mobile arms.

NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results

This paper presents the NTIRE 2025 image super-resolution ($\times$4) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025. The challenge aims to recover high-resolution (HR) images from low-resolution (LR) counterparts generated through bicubic downsampling with a $\times$4 scaling factor. The objective is to develop effective network designs or solutions that achieve state-of-the-art SR performance. To reflect the dual objectives of image SR research, the challenge includes two sub-tracks: (1) a restoration track, emphasizes pixel-wise accuracy and ranks submissions based on PSNR; (2) a perceptual track, focuses on visual realism and ranks results by a perceptual score. A total of 286 participants registered for the competition, with 25 teams submitting valid entries. This report summarizes the challenge design, datasets, evaluation protocol, the main results, and methods of each team. The challenge serves as a benchmark to advance the state of the art and foster progress in image SR.

FGSGT: Saliency-Guided Siamese Network Tracker Based on Key Fine-Grained Feature Information for Thermal Infrared Target Tracking

Thermal infrared (TIR) images typically lack detailed features and have low contrast, making it challenging for conventional feature extraction models to capture discriminative target characteristics. As a result, trackers are often affected by interference from visually similar objects and are susceptible to tracking drift. To address these challenges, we propose a novel saliency-guided Siamese network tracker based on key fine-grained feature infor-mation. First, we introduce a fine-grained feature parallel learning convolu-tional block with a dual-stream architecture and convolutional kernels of varying sizes. This design captures essential global features from shallow layers, enhances feature diversity, and minimizes the loss of fine-grained in-formation typically encountered in residual connections. In addition, we propose a multi-layer fine-grained feature fusion module that uses bilinear matrix multiplication to effectively integrate features across both deep and shallow layers. Next, we introduce a Siamese residual refinement block that corrects saliency map prediction errors using residual learning. Combined with deep supervision, this mechanism progressively refines predictions, ap-plying supervision at each recursive step to ensure consistent improvements in accuracy. Finally, we present a saliency loss function to constrain the sali-ency predictions, directing the network to focus on highly discriminative fi-ne-grained features. Extensive experiment results demonstrate that the pro-posed tracker achieves the highest precision and success rates on the PTB-TIR and LSOTB-TIR benchmarks. It also achieves a top accuracy of 0.78 on the VOT-TIR 2015 benchmark and 0.75 on the VOT-TIR 2017 benchmark.